Tens of Thousands of American Academics on the Editorial Boards of Journals Run by Predatory Publishers
Bibliographic record
Abstract
Between August 2022 and March 2023, the names and affiliations of 2183 American academics were found on the editorial boards of journals owned by nineteen predatory publishers. A survey email went out to 905 of them, and 195 replies were received. Twenty-six replies were uninformative. Sixty-five respondents confirmed that they served knowingly and willingly, ninety-one reported that their names were being used without their knowledge, and thirteen said that their names were being used despite requests to have them removed. Based on these results, a minimum estimate of the number of academics serving on the editorial boards of corrupt publishers runs into the tens of thousands, while the maximum exceeds ninety thousand. The former estimate uses very stringent criteria to define a predatory publisher, excluding all moderate, minor, and doubtful cases. This situation should be intolerable. The academic community is urged to take legal action.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".